Transfer learning approach may be generally represented by two techniques – feature extraction and fine tuning [1]}. Feature extraction is the procedure in which parameters of several donor models’ layers are frozen during training on acceptor dataset, which causes the layers to behave like feature extraction algorithm for target objects [2]}, [3]}. Fine tuning donor model generally retrains acceptor dataset as training set and models’ parameters obtained during previous training usually act as initial. In this work we used both techniques.
